Population coding

This work looked at population coding from an angle which has been
surprisingly neglected: time. What happens to the structure of the code if
stimuli are not static, but move around? We showed that interpretation of
spikes in a dynamic, fast-changing world is impossible without a prior over
how stimuli change. Depending on the nature of this prior, the code can be
computationally very unwieldy, but it can still be handled approximately by
recurrent neural networks.

Single-cell models

Building detailed, biophysically realistic single-cell models remains a
major challenge. The nature of the parametrization of these models results in
extremely complex, non-linear interactions between the various parameters. We
here build on recent advances in imaging techniques, which will soon allow
access to the transmembrane voltage at many points throughout a cell's
dendritic arbor. Given this rich information, and some more constraints, it
is possible to set many of the parameters of interests in an automatic way.